The background description provided herein is for the purpose of generally presenting the context of the present invention. The subject matter discussed in the background of the invention section should not be assumed to be prior art merely as a result of its mention in the background of the invention section. Similarly, a problem mentioned in the background of the invention section or associated with the subject matter of the background of the invention section should not be assumed to have been previously recognized in the prior art. The subject matter in the background of the invention section merely represents different approaches, which in and of themselves may also be inventions. Work of the presently named inventors, to the extent it is described in the background of the invention section, as well as aspects of the description that may not otherwise qualify as prior art at the time of filing, are neither expressly nor impliedly admitted as prior art against the present invention.
Ultrasound is ubiquitous as an interventional and diagnostic imaging modality, and is commonly used to assess the location and geometry of disease intraoperatively. An inherent problem with this role is the shape distortion of visualized tissue structures introduced by the probe pressure exerted. It is widely recognized that relatively large tissue compression can occur in soft tissue anatomy, e.g. the liver or breast. As a result, compression can obfuscate geometrical and locational measurements of subsurface targets such as tumors. This is particularly a problem for image-guided interventions which rely upon tracked ultrasound to provide intraoperative spatial measurements of structures taken during an intervention and then compared to their co-registered preoperative imaging data counterparts. Nonrigid tissue compression is a primary cause of misalignment and shape distortion with these other sources of information. As image-guided navigation strategies in soft tissue environments continue to be developed, methods of correcting the tissue deformation from routine ultrasound imaging are necessary in order to ensure that all of these data are in a consistent spatial arrangement.
There are several methods described in the literature for performing compression correction. A common approach is to utilize the intensity information in the ultrasound images to perform a nonrigid intensity-based registration with positional tracking of compressed images over a range of compression states [1, 2]. One drawback of this method is that it requires a stream of ultrasound images, and intensity based registration for ultrasound is a challenging task in practice. For example, Treece et al. [1] demonstrated a method to correct for compression using correlation of a stream of radiofrequency (RF) or amplitude frames, and although the method performed well in a phantom dataset, the authors noted its reliance on good image quality as well as the possibility of correction drift when compression estimates are accumulated across a large sequence of images. Another method of correction is to use a mechanical model of the tissue in order to estimate the subsurface tissue displacements caused by the interaction of the probe with the tissue surface. One group proposed using a force measurement apparatus to provide force boundary conditions to a tissue model [3, 4], although force boundary conditions require some prior estimate of absolute material properties for the tissue. An alternative method has recently been proposed, which utilizes a biomechanical model based correction which is driven by displacement boundary conditions provided by the position of a tracked ultrasound probe within a co-registered patient-specific organ surface from preoperative tomograms [5]. This method was shown to reduce ultrasound compressional error of nearly 1 cm to approximately 2 to 3 mm.
There is a subset of image-guided procedures for which preoperative tomographic image volumes are not commonly acquired, or the volumes are acquired with the patient in a much different presentation than the operative state. This can be the case in open liver surgery, for example, in which there is often significant manipulation of the organ by the surgeon leading up to the surgical presentation of the tissue. Therefore, a method of compression compensation that does not rely on a preoperative model would be more valuable. In addition, it is often the case that subsurface structures may be necessary for enhancing image-to-physical registration, and it is easily seen that there are implications if subsurface deformation is not addressed in registration frameworks. Provided with at least some form of intraoperative measurement of compression, subsurface structures could be uncompressed to give true shapes in physical space. These true subsurface shapes could then be used in combination with surface information to compute a combined image-to-physical registration. An example of this would be a registration framework that used a surface point cloud from a laser measurement device and subsurface structures like a tumor [6] or perhaps blood vessels [7]. This is just one embodiment of how true-shape subsurface tissue structures as described by an uncompressed ultrasound imaging technique could be used.
Therefore, a heretofore unaddressed need exists in the art to address the aforementioned deficiencies and inadequacies.